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BROADNETS
2004
IEEE

Efficient QoS Provisioning for Adaptive Multimedia in Mobile Communication Networks by Reinforcement Learning

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Efficient QoS Provisioning for Adaptive Multimedia in Mobile Communication Networks by Reinforcement Learning
The scarcity and large fluctuations of link bandwidth in wireless networks have motivated the development of adaptive multimedia services in mobile communication networks, where it is possible to increase or decrease the bandwidth of individual ongoing flows. This paper studies the issues of quality of service (QoS) provisioning in such systems. In particular, call admission control and bandwidth adaptation are formulated as a constrained Markov decision problem. The rapid growth in the number of states and the difficulty in estimating state transition probabilities in practical systems make it very difficult to employ classical methods to find the optimal policy. We present a novel approach that uses a form of discounted reward reinforcement learning known as Q-learning to solve QoS provisioning for wireless adaptive multimedia. Q-learning does not require the explicit state transition model to solve the Markov decision problem; therefore more general and realistic assumptions can be...
Fei Yu, Vincent W. S. Wong, Victor C. M. Leung
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2004
Where BROADNETS
Authors Fei Yu, Vincent W. S. Wong, Victor C. M. Leung
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